"""simple docstring"""


import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


a				={
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   ={}
						state_dict.pop("pixel_mean"  ,       __lowerCAmelCase							)
						state_dict.pop("pixel_std"  ,       __lowerCAmelCase							)

						lowerCamelCase__   =r".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

						for key, value in state_dict.items():
												for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
																		if key_to_modify in key:
																								lowerCamelCase__   =key.replace(__lowerCAmelCase  ,       __lowerCAmelCase							)

												if re.match(__lowerCAmelCase  ,       __lowerCAmelCase							):
																		lowerCamelCase__   =int(re.match(__lowerCAmelCase  ,       __lowerCAmelCase							).group(2							)							)
																		if layer_nb == 0:
																								lowerCamelCase__   =key.replace("layers.0"  ,       "proj_in"							)
																		elif layer_nb == 1:
																								lowerCamelCase__   =key.replace("layers.1"  ,       "layers.0"							)
																		elif layer_nb == 2:
																								lowerCamelCase__   =key.replace("layers.2"  ,       "proj_out"							)

												lowerCamelCase__   =value

						lowerCamelCase__   =model_state_dict[
						    "prompt_encoder.shared_embedding.positional_embedding"
						]

						return model_state_dict





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase="ybelkada/segment-anything"							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =hf_hub_download(__lowerCAmelCase  ,       F'''checkpoints/{model_name}.pth'''							)

						if "sam_vit_b" in model_name:
												lowerCamelCase__   =SamConfig()
						elif "sam_vit_l" in model_name:
												lowerCamelCase__   =SamVisionConfig(
												    hidden_size=1024  ,       num_hidden_layers=24  ,       num_attention_heads=16  ,       global_attn_indexes=[5, 11, 17, 23]  ,       )

												lowerCamelCase__   =SamConfig(
												    vision_config=__lowerCAmelCase  ,       )
						elif "sam_vit_h" in model_name:
												lowerCamelCase__   =SamVisionConfig(
												    hidden_size=1280  ,       num_hidden_layers=32  ,       num_attention_heads=16  ,       global_attn_indexes=[7, 15, 23, 31]  ,       )

												lowerCamelCase__   =SamConfig(
												    vision_config=__lowerCAmelCase  ,       )

						lowerCamelCase__   =torch.load(__lowerCAmelCase  ,       map_location="cpu"							)
						lowerCamelCase__   =replace_keys(__lowerCAmelCase							)

						lowerCamelCase__   =SamImageProcessor()

						lowerCamelCase__   =SamProcessor(image_processor=__lowerCAmelCase							)
						lowerCamelCase__   =SamModel(__lowerCAmelCase							)

						hf_model.load_state_dict(__lowerCAmelCase							)
						lowerCamelCase__   =hf_model.to("cuda"							)

						lowerCamelCase__   ="https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
						lowerCamelCase__   =Image.open(requests.get(__lowerCAmelCase  ,       stream=__lowerCAmelCase							).raw							).convert("RGB"							)

						lowerCamelCase__   =[[[400, 650]]]
						lowerCamelCase__   =[[1]]

						lowerCamelCase__   =processor(images=np.array(__lowerCAmelCase							)  ,       return_tensors="pt"							).to("cuda"							)

						with torch.no_grad():
												lowerCamelCase__   =hf_model(**__lowerCAmelCase							)
						lowerCamelCase__   =output.iou_scores.squeeze()

						if model_name == "sam_vit_h_4b8939":
												assert scores[-1].item() == 0.579_8902_5115_9668

												lowerCamelCase__   =processor(
												    images=np.array(__lowerCAmelCase							)  ,       input_points=__lowerCAmelCase  ,       input_labels=__lowerCAmelCase  ,       return_tensors="pt"							).to("cuda"							)

												with torch.no_grad():
																		lowerCamelCase__   =hf_model(**__lowerCAmelCase							)
												lowerCamelCase__   =output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9712_6030_9219_3604

												lowerCamelCase__   =((75, 275, 1725, 850),)

												lowerCamelCase__   =processor(images=np.array(__lowerCAmelCase							)  ,       input_boxes=__lowerCAmelCase  ,       return_tensors="pt"							).to("cuda"							)

												with torch.no_grad():
																		lowerCamelCase__   =hf_model(**__lowerCAmelCase							)
												lowerCamelCase__   =output.iou_scores.squeeze()

												assert scores[-1].item() == 0.8686_0156_0592_6514

												# Test with 2 points and 1 image.
												lowerCamelCase__   =[[[400, 650], [800, 650]]]
												lowerCamelCase__   =[[1, 1]]

												lowerCamelCase__   =processor(
												    images=np.array(__lowerCAmelCase							)  ,       input_points=__lowerCAmelCase  ,       input_labels=__lowerCAmelCase  ,       return_tensors="pt"							).to("cuda"							)

												with torch.no_grad():
																		lowerCamelCase__   =hf_model(**__lowerCAmelCase							)
												lowerCamelCase__   =output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
	a				=argparse.ArgumentParser()
	a				=['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
	parser.add_argument(
	    '--model_name',
	    default='sam_vit_h_4b8939',
	    choices=choices,
	    type=str,
	    help='Path to hf config.json of model to convert',
	)
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model and processor to the hub after converting',
	)
	parser.add_argument(
	    '--model_hub_id',
	    default='ybelkada/segment-anything',
	    choices=choices,
	    type=str,
	    help='Path to hf config.json of model to convert',
	)

	a				=parser.parse_args()

	convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)



"""simple docstring"""


from .imports import is_rich_available


if is_rich_available():
	from rich.traceback import install

	install(show_locals=False)

else:
	raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')


